Fairness and Diversity of Decision Making

Neil D. Lawrence

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at Center for the Advanced Studies of the Behavioral Sciences, Joint American Academy and Royal Society Workshop on Nov 8, 2018 [jupyter][google colab][reveal]

Neil D. Lawrence, Amazon Cambridge and University of Sheffield

Links

Related Blog Post

Abstract

Mathematical definitions of fairness insist on clearly categorized groups and clear mathematical interpretations of fairness. In law this arises through the concept of unlawful descrimination. There is no such thing as a correct model. We must accept that our predictions will sometimes be wrong. In the face of this certainty we have a choice: how we should be wrong. We can choose to be wrong by over-simplifying or we can choose to be wrong by over-complicating (given the available data). In machine learning this is known as the bias-variance dilemma. In this talk we consider the implications of the bias-variance dilemma for fairness of decision making.

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.

What is Machine Learning? [edit]

Machine learning allows us to extract knowledge from data to form a prediction.

$$\text{data} + \text{model} \xrightarrow{\text{compute}} \text{prediction}$$

A machine learning prediction is made by combining a model with data to form the prediction. The manner in which this is done gives us the machine learning algorithm.

Machine learning models are mathematical models which make weak assumptions about data, e.g. smoothness assumptions. By combining these assumptions with the data we observe we can interpolate between data points or, occasionally, extrapolate into the future.

Machine learning is a technology which strongly overlaps with the methodology of statistics. From a historical/philosophical view point, machine learning differs from statistics in that the focus in the machine learning community has been primarily on accuracy of prediction, whereas the focus in statistics is typically on the interpretability of a model and/or validating a hypothesis through data collection.

The rapid increase in the availability of compute and data has led to the increased prominence of machine learning. This prominence is surfacing in two different, but overlapping domains: data science and artificial intelligence.

The real challenge, however, is end-to-end decision making. Taking information from the enviroment and using it to drive decision making to achieve goals.